Advancing aviation safety through machine learning and psychophysiological data: a systematic review

In the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior

American and Chinese Personality Traits and Task Load in Simulated Flight Crews: Individual and Team Level Effects

Understanding the impact of pilot interpersonal dynamics may be crucial for flight team success as well as the prevention of air crash disasters. Achieving optimum performance from flight teams requires limiting unnecessary pilot task load. This study examined American and Chinese simulated flight crews. Factors believed to affect cockpit interpersonal dynamics and subsequent crew task loads were pilot personality and nationality. Pilot personality, team personality elevation, team personality variability, and team nationality were analyzed for their potential impact on task load perceptions. Twenty-four American, 23 Chinese, and 23 mixed nationality two person teams were created and used for comparisons. Increasing level of openness to experience was found to significantly decrease pilot perceptions of task load at the individual level of analysis. American teams were found to experience significantly overall lower task load perceptions than Chinese teams. These findings may have implications for training and safety protocol for pilots. Limitations of this study and suggestions for future research are discussed

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 315)

This bibliography lists 211 reports, articles and other documents introduced into the NASA scientific and technical information system in September, 1988

Complex Assessment of Pilot Fatigue in Terms of Physiological Parameters

Únava pilotů je jedním z hlavních důvodů leteckých nehod, ke kterým došlo v důsledku pochybení lidského činitele. Z tohoto důvodu je v zájmu zachování nejvyšších standardů letové bezpečnosti ve všech fázích letu zásadní být schopen zabránit vzniku únavy nebo alespoň být schopen ji účinně detekovat a následně na tuto skutečnost upozornit posádku, aby byla schopna unaveného člena posádky odstavit. V současnosti existují studie zabývající se detekcí a sledováním únavy pilotů prostřednictvím fyziologických parametrů, jako je srdeční aktivita, pohyby očí, aktivita horních končetin apod. Ze všech dostupných fyziologických měření se pak analýza variability srdečního rytmu (HRV) jeví jako nejvhodnější metoda zkoumání únavy pilota. Ačkoli se k hodnocení únavy používá mnoho parametrů vycházejících z analýzy variability srdečního rytmu, v literatuře neexistuje shoda o tom, které z těchto parametrů variability srdeční frekvence jsou nejdůležitější pro použití při detekci únavy piloty. Na základě tohoto nedostatku informací v kontextu současného stavu poznání je cílem této práce zjistit nejvýznamnější parametry analýzy variability srdečního rytmu, které lze přímo použít při monitorování únavy pilota. Pro účely zisku dat byly provedeny 24hodinové experimenty, při nichž byla sbírána data o srdeční aktivitě 16 subjektů na Ústavu letecké dopravy, Fakulty dopravní, Českého vysokého učení technického v Praze. Údaje o srdeční aktivitě subjektu byly zaznamenány ve formě elektrokardiogramu (EKG), zatímco plnily letové úkoly. První část této práce přináší teoretické základy únavy v prostředí kokpitu a vysvětluje několik metod, které se používají pro analýzu variability srdeční frekvence zaznamenaných signálů EKG. Následující části obsahují metody statistické analýzy používané k zjištění parametrů s nejvyšší importancí. Výsledky naznačují, že parametr pVLF analýzy ve frekvenční a časově-frekvenční doméně a parametr nHF analýzy HRV ve frekvenční doméně jsou parametry s nejvyšší importancí v případě indikace únavy člena letové posádky. Klíčová slova: Únava pilota, fyziologické parametry, srdeční aktivita, variabilita srdečního rytmuPilot fatigue is one of the main reasons of aircraft accidents that were caused due to the human error factors in flight crew. Therefore, in order to maintain the highest standards of flight safety throughout all flight phases, it is crucially important to be able to prevent occurrence of fatigue or at least to be able to efficiently detect it, afterwards alert the crew to eliminate the fatigued member from flying. At present, there are many studies focusing on detection and monitoring of pilot fatigue by tracking pilot’s physiological parameters such as: cardiac activity, eye movements, upper-limb activities etc. Among all those physiological measurements available, heart rate variability analysis seems to be the most accurate method to examine pilot fatigue. Although many indices of heart rate variability analysis are used to evaluate fatigue, there is no consensus in the literature on which of those heart rate variability indices are the most important ones to utilize on determination of pilot fatigue. Based on this lack of information on the current state of the art, the purpose of this thesis is to ascertain the most significant parameters of heart rate variability analysis that can be directly used in determining pilot fatigue. For obtaining data, a 24-hours of cardiac activity measurements were conducted on 16 subjects on a flight simulator located at the Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague. The subject’s cardiac activity data were recorded in form of electrocardiogram (ECG) while they performed flying tasks. The first part of this thesis delivers a theoretical background on fatigue in the cockpit environment and explains several methods that are used for heart rate variability analysis of the recorded ECG signals. The following parts provide the statistical analysis methods used to find out the most important parameters. The results indicate that pVLF index of the frequency domain and time-frequency domain analysis and nHF parameter of frequency-domain analysis of HRV corresponds to the most important indices which indicate fatigued condition of a flight crew member. Keywords: Pilot fatigue, physiological parameters, cardiac activity, heart rate variabilit

The flight of information: New approaches for investigating aviation accident causation

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The investigation and modelling of aviation accident causation is dominated by linear models. Aviation is, however, a complex system and as such suffers from being artificially manipulated into non-complex models and methods. This thesis addresses this issue by developing a new approach to investigating aviation accident causation through information networks. These networks centralise communication and the flow of information as key indicators of a system‟s health and risk. The holistic approach focuses on the system itself rather than any individual event. The activity and communication of constituent elements, both human and non-human agents, within that system is identified and highlights areas of system failure. The model offers many potential developments and some key areas are studied in this research. Through the centralisation of barriers and information nodes the method can be applied to almost any situation. The application of Bayesian mathematics to historical data populations provides scope for studying error migration and barrier manipulation. The thesis also provides application of these predictions to a flight simulator study in an attempt of validation. Beyond this the thesis also discusses the applicability of the approach to industry. Through working with a legacy airline the methods discussed are used as the basis for a new and forward-thinking safety management system. This holistic approach focuses on the system environment, the activity that takes place within it, the strategies used to conduct this activity, the way in which the constituent parts of the system (both human and non-human) interact and the behaviour required. Each stage of this thesis identifies and expands upon the potential of the information network approach maintaining firm focus on the overall health of a system. It is contended that through the further development and application of this approach, understanding of aviation risk can be improved

The flight of information : new approaches for investigating aviation accident causation

The investigation and modelling of aviation accident causation is dominated by linear models. Aviation is, however, a complex system and as such suffers from being artificially manipulated into non-complex models and methods. This thesis addresses this issue by developing a new approach to investigating aviation accident causation through information networks. These networks centralise communication and the flow of information as key indicators of a system‟s health and risk. The holistic approach focuses on the system itself rather than any individual event. The activity and communication of constituent elements, both human and non-human agents, within that system is identified and highlights areas of system failure. The model offers many potential developments and some key areas are studied in this research. Through the centralisation of barriers and information nodes the method can be applied to almost any situation. The application of Bayesian mathematics to historical data populations provides scope for studying error migration and barrier manipulation. The thesis also provides application of these predictions to a flight simulator study in an attempt of validation. Beyond this the thesis also discusses the applicability of the approach to industry. Through working with a legacy airline the methods discussed are used as the basis for a new and forward-thinking safety management system. This holistic approach focuses on the system environment, the activity that takes place within it, the strategies used to conduct this activity, the way in which the constituent parts of the system (both human and non-human) interact and the behaviour required. Each stage of this thesis identifies and expands upon the potential of the information network approach maintaining firm focus on the overall health of a system. It is contended that through the further development and application of this approach, understanding of aviation risk can be improved.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications